Flexible Processing Capabilities Research Articles

Evaluation Model of Physical Education Teaching Effect Based on Embedded Neural Network

In response to the uniqueness of sports learning evaluation, we innovatively chose neural networks as the modeling basis and constructed a sports teaching effectiveness evaluation model based on embedded neural networks. This model achieves intelligent and automated evaluation of physical education teaching through the flexible processing capability of an efficient learning ability logic system embedded with neural networks. Specifically, embedded neural networks can deeply mine complex data during the teaching process. The embedded neural network effectively solves the uncertainty in the evaluation process and significantly improves the accuracy and adaptability of the evaluation system. In the process of model construction, we pay special attention to the adaptive processing capability of data. By introducing an adaptive neural system, the model can dynamically adjust parameters to better adapt to different teaching scenarios and individual differences among students, ensuring the objectivity and impartiality of evaluation results. This adaptability not only enhances the flexibility of the evaluation system, but also provides solid support for its application in practical teaching.

Manufacturing cell formation with flexible processing capabilities and worker assignment: Comparison of constraint programming and integer programming approaches

Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. Constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-machine requirements, availabilities in the system in terms of capacity, machine and worker abilities. In this study, the cell formation problem is modeled using machine, part processing and worker flexibilities via resource element–based representation. Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. A total of 12 case problems are generated, and different search phases of constraint programming are defined for the solution procedure. The cell formation problem is modeled in both constraint programming and integer programming, and a comparative analysis of constraint programming and integer programming model solutions is done. The results indicate that both the models are effective and efficient in the solution of the cell formation problem.

A multi-agent based approach to dynamic scheduling with flexible processing capabilities

A multi-agent based system is proposed to simultaneous scheduling of flexible machine groups and material handling system working under a manufacturing dynamic environment. The proposed model is designed by means of \(\hbox {Prometheus}^{\mathrm{TM}}\) methodology and programmed in \(\hbox {JACK}^{\mathrm{TM}}\) agent based systems development environment. Each agent in the model is autonomous and has an ability to cooperate and negotiate with the other agents in the system. Due to these abilities of agents, the structure of the system is more suitable to handle dynamic events. The proposed dynamic scheduling system is tested on several test problems the literature and the results are quite satisfactory because it generates effective schedules for both dynamic cases in the real time and static problem sets. Although the model is designed as an online method and has a dynamic structure, obtained schedule performance parameters are very close to those obtained from offline optimization based algorithms.

Logo recognition based on membership degree and closeness degree of fuzzy sets

Fuzzy information theory which is based on fuzzy theory is a departure from information science. Higher performance could be obtained in pattern recognition with fuzzy sets theory. People combine fuzzy sets theory with pattern recognition and established fuzzy pattern recognition. This paper presents our work in the field of logo recognition by using fuzzy sets theory. Aim at the application requirement of logo recognition in image intelligent processing, a logo recognition algorithm based on membership degree and closeness degree of fuzzy sets is proposed. By fuzzy mapping, the logo's gridding feature is transformed to the membership degree of fuzzy sets; then using closeness degree and closest principle of fuzzy sets to accomplish logo recognition. The algorithm significantly enhances the adaptability and anti-interference to poor quality images and effectively improves the flexible processing capability of logo recognition system. Experimental results show that the recognition rate of this logo recognition algorithm can reach as high as 94.5%.

Analysing the effect of flexibility on manufacturing systems performance

PurposeIn today's very competitive, dynamic and unpredictable manufacturing environments it is critical to improve manufacturing performance in order to be able to compete. Responsiveness and agility become important characteristics of manufacturing systems and organizations. Manufacturing systems must be designed optimally by taking into account responsiveness and agility related measures in order to improve effectiveness and performance. One of the important enablers of performance improvement is flexibility. It is a known fact that flexibility has a positive effect on the manufacturing system performance if it is properly utilized by the control system (usually scheduling). However, the relationship between flexibility and manufacturing system performance through scheduling is not entirely explored in the previous literature. The purpose of this paper is to investigate the effects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops.Design/methodology/approachEffects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops are analyzed at different flexibility levels by using the grammar‐based flexible job shop scheduling system that is developed by Baykasoğlu et al.. Three different flexibility levels are defined for process plans and machines. Four different problem sizes are evaluated according to “makespan” “machine load balance” and “mean waiting times of jobs”. Performance differences among “process plan” and “machine flexibility” levels are determined and statistically analyzed through Taguchi experimental design methodology.FindingsIt is found out after detailed analysis that the effect of machine flexibility on job shop performance is higher than the process plan flexibility. It is also figured out that after a certain level of machine flexibility, the speed of scheduling performance improvement decreases considerably.Originality/valueThe paper presents the interaction between flexibility and scheduling performance of manufacturing job‐shops. The findings should be taken into account while designing scheduling systems for job shops that have flexible processing capabilities.